Module 1 - What is Machine Learning

Overview and Deliverables

  • 1/26: Attend the initial course meetup at 6:45PM Eastern
  • Due 2/01: Introduction Post in Brightspace Discussions
  • Join Course Slack channel
  • Install a suitable python IDE if you do not have one (for instance positron), and a basic python installation (see the software page

Learning Objectives

  • Examples of Machine Learning Problems
  • Defining Supervised Learning, Unsupervised Learning, Reinforcement Learning
  • Goals of Machine Learning: Prediction, Understanding (inference/causal inference), Decision Making
  • The components of the machine learning problem
  • Review course toolkit (or learn if unfamiliar): numpy, pandas, scikit-learn, matplotlib

Readings

Interactive Lab

  • ISLP (Introduction to Statistical Learning): [Section 2.3: Introduction to Python]

Videos

The book authors have a video playlist where they go through the book section by section. You may or may not like they style and presentation- my weekly videos will be different than theirs. I will also make videos of coding demonstrations. For this week the relevant links are:

Link to All Python Labs

Link to All Course Videos

Videos Covering Material Relevant to this week:

Opening Remarks Updated Opening Remarks Statistical Learning Examples and Framework 1.2 Statistical Learning Intro to Regression Models 2.1

Coding Videos Relevant to this week: